标题： RadioDiff-3D：一个3D$\times$3D无线电图数据集以及基于生成扩散的6G环境感知通信基准 显示英文标题

标题： RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication

摘要： 无线电图（RMs）是实现环境感知无线通信的关键基础，因为它们提供了无线信道特性的空间分布。 尽管在使用数据驱动方法进行RM构建方面取得了进展，但大多数现有方法仅关注固定二维平面上的路径损耗预测，忽略了到达方向（DoA）、到达时间（ToA）和垂直空间变化等关键参数。 这种限制主要是由于依赖静态学习范式，这阻碍了在训练数据分布之外的泛化能力。 为了解决这些挑战，我们提出了UrbanRadio3D，这是一个通过在真实城市环境中进行射线追踪构建的大规模、高分辨率3D RM数据集。 UrbanRadio3D在三维空间中比之前的数据集大超过37$\times$3倍，包含路径损耗、DoA和ToA三个指标，形成了一个具有7$\times$3个更多高度层的新型3D$\times$33D数据集，比之前最先进的（SOTA）数据集多。 为了基准测试3D RM的构建，提出了一种使用3D卷积算子的UNet。 此外，我们进一步引入了 RadioDiff-3D，一种基于扩散模型的生成框架，利用3D卷积架构。 RadioDiff-3D支持已知发射器位置的辐射感知场景以及基于稀疏空间观测的辐射非感知设置。 在UrbanRadio3D上的广泛评估验证了RadioDiff-3D在多样环境动态下构建丰富、高维无线电图方面的优越性能。 这项工作为未来3D环境感知通信的研究提供了基础数据集和基准。 该数据集可在https://github.com/UNIC-Lab/UrbanRadio3D获取。 显示英文摘要

摘要： Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the reliance on static learning paradigms, which hinder generalization beyond the training data distribution. To address these challenges, we propose UrbanRadio3D, a large-scale, high-resolution 3D RM dataset constructed via ray tracing in realistic urban environments. UrbanRadio3D is over 37$\times$3 larger than previous datasets across a 3D space with 3 metrics as pathloss, DoA, and ToA, forming a novel 3D$\times$33D dataset with 7$\times$3 more height layers than prior state-of-the-art (SOTA) dataset. To benchmark 3D RM construction, a UNet with 3D convolutional operators is proposed. Moreover, we further introduce RadioDiff-3D, a diffusion-model-based generative framework utilizing the 3D convolutional architecture. RadioDiff-3D supports both radiation-aware scenarios with known transmitter locations and radiation-unaware settings based on sparse spatial observations. Extensive evaluations on UrbanRadio3D validate that RadioDiff-3D achieves superior performance in constructing rich, high-dimensional radio maps under diverse environmental dynamics. This work provides a foundational dataset and benchmark for future research in 3D environment-aware communication. The dataset is available at https://github.com/UNIC-Lab/UrbanRadio3D.